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Selected model is at capacity on every model despite available usage quota #44558

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Description

@javierspinoza

What version of the Codex App are you using (From “About Codex” dialog)?

Codex CLI 0.154.0

What subscription do you have?

ChatGPT Plus

What platform is your computer?

Linux 7.0.0-31-generic x86_64 desconocido

What issue are you seeing?

Codex CLI is currently unusable because every model I select returns the same error:

Selected model is at capacity. Please try a different model.

This happens even though I still have significant usage quota available.

At the time of the issue, Codex showed approximately:

  • 5h: 83% left
  • Weekly: 34% left
  • Context: 0% used
  • Context window: 400K

The issue is not specific to GPT-5.6 Sol. I have switched between multiple available models and reasoning levels, but they all return the same capacity error.

A fresh conversation with 0% context usage also fails immediately.

codex doctor reports a healthy installation:

  • 19 ok
  • 0 warn
  • 0 fail
  • Authentication configured correctly
  • WebSocket connected successfully (HTTP 101 Switching Protocols)
  • OpenAI/ChatGPT inference endpoint reachable
  • Local state databases healthy
  • No proxy configured

This suggests the issue is not caused by local connectivity, installation, authentication, context exhaustion, or normal usage-limit exhaustion.

Image

What steps can reproduce the bug?

  1. Open Codex CLI on Ubuntu Linux.
  2. Start a new session with a fresh context.
  3. Select any available model, for example GPT-5.6 Sol High.
  4. Send any simple prompt.
  5. Codex immediately returns:

Selected model is at capacity. Please try a different model.

  1. Use /model and switch to another available model.
  2. Send another prompt.
  3. The same error appears.
  4. Repeat with other available models and reasoning levels. The error occurs with every model tested.

At the time of testing:

  • Codex CLI: 0.154.0
  • 5-hour usage: 83% left
  • Weekly usage: 34% left
  • Context usage: 0%
  • Context window: 400K

The issue also occurs in completely new sessions with 0% context usage.

What is the expected behavior?

Codex should process requests normally while usage quota is still available.

If a specific model is temporarily at capacity, switching to another available model should allow the request to continue.

Instead, every available model I have tested returns:

Selected model is at capacity. Please try a different model.

This happens even in a fresh session with 0% context usage and significant usage quota remaining.

Additional information

Environment:

  • Codex CLI: 0.154.0
  • Subscription: ChatGPT Plus
  • OS: Ubuntu 26.04 (resolute), 64-bit
  • Kernel: Linux 7.0.0-31-generic
  • Architecture: x86_64

I ran codex doctor while the issue was occurring.

Result:

19 ok · 1 idle · 0 warn · 0 fail

Important diagnostic results:

  • Installation: consistent
  • Authentication: configured correctly
  • Local databases: healthy
  • WebSocket: connected successfully (HTTP 101 Switching Protocols)
  • OpenAI provider endpoint: reachable
  • No proxy configured
  • Codex version: current (0.154.0)

The error persists across different models, reasoning levels, and completely new sessions.

Because all tested models fail while usage quota remains available and codex doctor reports no warnings or failures, the issue may be related to service-side capacity, account routing, or model admission rather than my local installation or network connection.

Activity

  1. added
    bugSomething isn't working
    CLIIssues related to the Codex CLI
    rate-limitsIssues related to rate limits, quotas, and token usage reporting
    on Sep 10, 2026
  2. yvgude commented on Sep 10, 2026

    @yvgude

    same here... since the whole day...

  3. ying0208 commented on Sep 16, 2026

    @ying0208

    I’m experiencing what appears to be the same issue on a ChatGPT Business account.

    Workspace name: FR

    Other users in the same Business workspace can use Codex normally, but my account consistently receives:

    Selected model is at capacity. Please try a different model.

    This happens across every available model I have tried, while my Codex usage allowance is still available.

    One important contrast: ChatGPT on the web works normally for the same account; only Codex is affected.

    I have already tried:

    • Updating Codex CLI
    • Logging out and signing back in with ChatGPT
    • Starting completely new sessions
    • Switching between multiple models
    • Using a Codex usage reset
    • Removing / disabling my custom model provider configuration

    The usage reset did not change the behavior. Codex still reports the same capacity error across models.

    Since ChatGPT web works for the same account, and other users in the same Business workspace can use Codex normally at the same time, this looks account-specific and Codex-specific rather than a workspace-wide or general account outage.

    This may be related to account-level Codex routing, entitlement, admission, or rate-limit state.

  4. ShylockAllan commented on Sep 21, 2026

    @ShylockAllan

    I am seeing what appears to be the same issue on Codex CLI 0.155.1 with ChatGPT Pro.

    Symptoms:

    • "Selected model is at capacity. Please try a different model."
    • reproduced on gpt-5.6-sol high and gpt-5.6-luna low
    • reproduced in a brand-new conversation with a very small prompt
    • weekly Codex allowance still has 57% remaining
    • authentication and WebSocket connectivity are healthy
    • failed requests show 0 input / 0 output / 0 total tokens
    • issue is intermittent; some requests succeed, others fail immediately

    Uploaded diagnostic thread:
    01a0c30a-e221-73b3-968f-6f4c44b256ce

  5. jichang1997-byte commented on Oct 13, 2026

    @jichang1997-byte

    I’m seeing the same recurring behavior on Codex Desktop for macOS (version 26.1007.30912, build 20156): “Selected model is at capacity. Please try a different model.” across multiple chats and multiple models. New chats can succeed briefly and then fail again, and the failures reproduce across separate sessions. A minimal diagnostic can sometimes succeed, suggesting intermittent model-admission or session-routing behavior rather than a simple local configuration problem. Please investigate the desktop session/model routing and capacity handling, and provide a clearer error or recovery path when all models are rejected.

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